Work

Improving Automated Energy Monitoring Systems

Year: 2025

Type: article

Abstract: Automated Energy Monitoring Systems (AEMS) are essential for efficient energy management but face challenges in data accuracy and processing speed. This study proposes a hybrid approach combining Kalman filtering for noise reduction and Long Short-Term Memory (LSTM) neural networks for predictive analytics to enhance AEMS performance. The methodology was tested on a 30-day dataset from a commercial building, achieving a 15% improvement in prediction accuracy and a 20% reduction in processing latency compared to baseline systems. These advancements enable more reliable and scalable energy monitoring, supporting sustainable energy management practices.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15287221

Authors: Dadajonov Muzaffar Navruzbek o'g'li, Yuldashev Elbek Ergash o'g'li

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W6949615620

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